This paper contributes a new boosting paradigm to achieve detection of events in video. Previous boosting paradigms in vision focus on single frame detection and do not scale to video events. Thus new concepts need to be introduced to address questions such as determining if an event has occurred, localizing the event, handling same action performed at different speeds, incorporating previous classifier responses into current decision, using temporal consistency of data to aid detection and recognition. The proposed method has the capability to improve weak classifiers by allowing them to use previous history in evaluating the current frame. A learning mechanism built into the boosting paradigm is also given which allows event level decisions to be made. This is contrasted with previous work in boosting which uses limited higher level temporal reasoning and essentially makes object detection decisions at the frame level. Our approach makes extensive use of temporal continuity of video at the classifier and detector levels. We also introduce a relevant set of activity features. Features are evaluated at multiple zoom levels to improve detection. We show results for a system that is able to recognize 11 actions.

This paper presents a system for analyzing human driver visual attention. The system relies on estimation of global motion and color statistics to robustly track a person's head and facial features. The system is fully automatic, it can initialize automatically, and reinitialize when necessary. The system classifies rotation in all viewing directions, detects eye/mouth occlusion, detects eye blinking and eye closure, and recovers the three dimensional gaze of the eyes. In addition, the system is able to track both through occlusion due to eye blinking, and eye closure, large mouth movement, and also through occlusion due to rotation. Even when the face is fully occluded due to rotation, the system does not break down. Further the system is able to track through yawning, which is a large local mouth motion. Finally, results are presented, and future work on how this system can be used for more advanced driver visual attention monitoring is discussed.

We describe a system for analyzing human driver alertness. It relies on optical flow and color predicates to robustly track a person's head and facial features. Our system classifies rotation in all viewing directions, detects eye/mouth occlusion, detects eye blinking, and recovers the 3D gaze of the eyes. We show results and discuss how this system can be used for monitoring driver alertness.